Neuroadaptive integral robust control of visual quadrotor for tracking a moving object

Abstract

Abstract This paper aims to achieve the visual quadrotor tracking for ground moving target in the presence of various uncertainties. In order to provide a stable quadrotor motion, the perspective image moments extracted from the defined virtual image plane are selected as the visual features to deduce a decoupled visual quadrotor model. Then a neuroadaptive integral robust controller is designed for the outer and inner loops of visual quadrotor within the image based visual servo (IBVS) framework, which enables error stabilization and improves the anti-disturbance capability. Then, the unknown lumped uncertainties consisting of parametric uncertainties and external disturbances are estimated via constructing a minimal learning parameter-neural network (MLP-NN) and compensated in the feedforward loop, subsequently the residual estimation errors are further suppressed by the structured robust integral of the sign of the error (RISE) feedback control law, hence a high-precise servo tracking performance can be expected. The innovations of the algorithm lie in, (1) with the NN approximation compensated in feedforward loop, the residual estimation errors in feedback loop are decreased, hence the conservatism that using high gains when facing large uncertainties is relaxed, (2) by means of the peculiarity of RISE control, asymptotic stability can be guaranteed with continuous and bounded control inputs, (3) using MLP technology, the number of NN learning parameters are decreased to only two for the closed-loop system, and the online computational load is greatly reduced. Finally, the validity and superiority of the proposed method are validated through affluent simulations and comparisons.